Overview

  • TensorFlow Basics

  • R Interfaces to TensorFlow

  • Deep Learning

  • Supporting Tools

  • Deployment

  • Learning More

What is TensorFlow?

A general purpose numerical computing library

Why should R users care about TensorFlow?

  • A new general purpose numerical computing library with many potential applications!

  • Models created with TensorFlow can be deployed with a low-latency C++ runtime

  • Robust foundation for many deep learning applications

  • R has a lot to offer as an interface language for TensorFlow

What are tensors?

Data stored in multidimensional arrays

Dimension R object
0D 42
1D c(42, 42, 42)
2D matrix(42, nrow = 2, ncol = 2)
3D array(42, dim = c(2,3,2))
4D array(42, dim = c(2,3,2,3))

Some examples

  • Vector data—2D tensors of shape (samples, features)

  • Timeseries or sequence data—3D tensors of shape (samples, timesteps, features)

  • Images—4D tensors of shape (samples, height, width, channels) ï‚¡

  • Video—5D tensors of shape (samples, frames, height, width, channels)

2D Tensors

Vector data

head(data.matrix(iris), n = 10)
      Sepal.Length Sepal.Width Petal.Length Petal.Width Species
 [1,]          5.1         3.5          1.4         0.2       1
 [2,]          4.9         3.0          1.4         0.2       1
 [3,]          4.7         3.2          1.3         0.2       1
 [4,]          4.6         3.1          1.5         0.2       1
 [5,]          5.0         3.6          1.4         0.2       1
 [6,]          5.4         3.9          1.7         0.4       1
 [7,]          4.6         3.4          1.4         0.3       1
 [8,]          5.0         3.4          1.5         0.2       1
 [9,]          4.4         2.9          1.4         0.2       1
[10,]          4.9         3.1          1.5         0.1       1

3D Tensors

Timeseries or sequence data

4D Tensors

Image data

What is tensor "flow"?

A dataflow graph with nodes representing units of computation

  • Parallelism–System identifies operations that can execute in parallel.
  • Distributed execution–Graph can be partitioned accross multiple devices.
  • Compilation–Use the information in your dataflow graph to generate faster code (e.g. fusing operations)
  • Portability–Dataflow graph is a language-independent representation of the code in your model (deploy with C++ runtime)

Transition to Greta

  • CRAN use of RcppArmadillo and RcppEigen

Example: Greta

Writing statistical models and fitting them by MCMC

R Interface to Tensorflow

  • High-level R interfaces for neural nets and traditional models

  • Low-level interface to allow enable new applications (e.g. Greta)

  • Tools to faciliate productive workflow / experiment management

  • Easy access to GPUs for training models

  • Breadth and depth of educational resources

Home Page Illustration

R Packages

too dense / premature

Package Description
keras Interface for neural networks, with a focus on enabling fast experimentation.
tfestimators Implementations of common model types such as regressors and classifiers.
tensorflow Low-level interface to the TensorFlow computational graph.
tfdatasets Scalable input pipelines for TensorFlow models.
tfruns Track, visualize, and manage TensorFlow training runs and experiments.
tfdeploy Tools designed to make exporting and serving TensorFlow models straightforward.
cloudml R interface to Google Cloud Machine Learning Engine.

  • High-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano (and soon MXNet).

  • Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

  • Supports both convolutional networks and recurrent networks, as well as combinations of the two.

  • Runs seamlessly on CPU and GPU.

  • https://keras.rstudio.com

Keras Adoption

Keras Concepts / Illustrations

First Look at Keras

Training a simple multi-layer perceptron (MLP)

library(keras)

model <- keras_model_sequential() %>% 
  layer_dense(units = 32, activation = 'relu', input_shape = c(100)) %>% 
  layer_dropout(rate = 0.2) %>% 
  layer_dense(units = 16, activation = 'relu') %>% 
  layer_dense(units = 1, activation = 'sigmoid') 

model %>% compile(
  optimizer = 'rmsprop',
  loss = 'binary_crossentropy',
  metrics = c('accuracy')
)

model %>% fit(
  x = data, y = labels, 
  epochs = 10, 
  batch_size = 32
)

Keras Layers

Operations that are chained together to progressively distill data

model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu',
                input_shape = input_shape) %>% 
  layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>% 
  layer_dropout(rate = 0.25) %>% 
  layer_flatten() %>% 
  layer_dense(units = 128, activation = 'relu') %>% 
  layer_dropout(rate = 0.5) %>% 
  layer_dense(units = num_classes, activation = 'softmax')

Keras Workflow – Data Preprocessing

library(keras)

# Load MNIST images datasets (built in to Keras)
c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()

# Flatten images and transform RGB values into [0,1] range 
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
x_train <- x_train / 255
x_test <- x_test / 255

# Convert class vectors to binary class matrices
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

Keras Workflow – Define Model

model <- keras_model_sequential()  %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

Keras Workflow – Define Model (cont.)

summary(model)
_____________________________________________________________________________________
Layer (type)                          Output Shape                      Param #      
=====================================================================================
dense_1 (Dense)                       (None, 256)                       200960       
_____________________________________________________________________________________
dropout_1 (Dropout)                   (None, 256)                       0            
_____________________________________________________________________________________
dense_2 (Dense)                       (None, 128)                       32896        
_____________________________________________________________________________________
dropout_2 (Dropout)                   (None, 128)                       0            
_____________________________________________________________________________________
dense_3 (Dense)                       (None, 10)                        1290         
=====================================================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
_____________________________________________________________________________________

Keras Workflow – Training

history <- model %>% fit(
  x_train, y_train,
  batch_size = 128,
  epochs = 30,
  validation_split = 0.2
)
history
Trained on 48,000 samples, validated on 12,000 samples (batch_size=128, epochs=30)
Final epoch (plot to see history):
     acc: 0.9057
    loss: 1.5
 val_acc: 0.9317
val_loss: 1.088

Keras Workflow – Training (cont.)

plot(history)

Keras Workflow – Evaluation and Prediction

model %>% evaluate(x_test, y_test)
$loss
[1] 0.1078904

$acc
[1] 0.9815
model %>% predict_classes(x_test[1:100,])
  [1] 7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7
 [36] 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0
 [71] 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9

TensorFlow Estimators

High level API for TensorFlow models https://tensorflow.rstudio.com/tfestimators/

Estimator Description
linear_regressor() Linear regressor model.
linear_classifier() Linear classifier model.
dnn_regressor() Dynamic nueral network regression.
dnn_classifier() Dynamic nueral network classification.
dnn_linear_combined_regressor() DNN Linear Combined Regression.
dnn_linear_combined_classifier() DNN Linear Combined Classification.

TensorFlow Core API

Low level access to TensorFlow graph operations https://tensorflow.rstudio.com/tensorflow/

W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))
y <- W * x_data + b

loss <- tf$reduce_mean((y - y_data) ^ 2)
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train <- optimizer$minimize(loss)

sess = tf$Session()
sess$run(tf$global_variables_initializer())

for (step in 1:200) {
  sess$run(train)
  if (step %% 20 == 0)
    cat(step, "-", sess$run(W), sess$run(b), "\n")
}

Learning more

https://tensorflow.rstudio.com/learn/

  • Recommended reading

  • Keras for R cheatsheet

  • Gallery and examples

  • Subscribe to the TensorFlow for R blog!

Recommended Reading

Keras for R cheatsheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

Gallery and examples

https://tensorflow.rstudio.com/learn/gallery.html

Thank you!

In Place Modification

Object semantics are not by-value! (as is conventional in R)

# Modify model object in place (note that it is not assigned back to)
model %>% compile(
  optimizer = 'rmsprop',
  loss = 'binary_crossentropy',
  metrics = c('accuracy')
)

  • Keras models are directed acyclic graphs of layers whose state is updated during training.

  • Keras layers can be shared by mutliple parts of a Keras model.